DeepSoluE

1. Description

Protein solubility is the precondition for its industrial application and functional interpretation. However, the formation of inclusion bodies is still an inevitable roadblock in protein science and industry, where only nearly a quarter of proteins can be successfully expressed in soluble form.

DeepSoluE is developed to predicts protein solubility using a long-short-term memory (LSTM) network with hybrid features composed of physicochemical patterns and distributed representation of amino acids. Comparison results showed that the proposed model achieved more accurate and balanced performance than existing tools.

2. Availability

2.1. Webserver is available at: http://39.100.246.211:10505/DeepSoluE/

2.2 Datasets and source code are available at:

http://lab.malab.cn/~wangchao/softs/DeepSoluE/ and https://github.com/wangchao-malab/DeepSoluE/.

2.3 Local running

2.3.1 Environment

Before running, please make sure the following packages are installed in Python environment:

gensim==3.4.0

pandas==1.1.3

tensorflow==2.3.0

python==3.7.3

biopython==1.7.8

numpy==1.19.2

For convenience, we strongly recommended users to install the Anaconda Python 3.7.3 (or above) in your local computer.

2.3.2 Additional software requirements

Two additional softwares, namely USEARCH and TMHMM, are needed for DeepSoluE, we did not provide the two tools in the source code packages because of the license restriction. The two methods can be acquired at the following links:

USEARCH: https://www.drive5.com/usearch/

TMHMM: https://services.healthtech.dtu.dk/cgi-bin/sw_request

For convenience, please extract the zip file to the “softs” folder of the DeepSoluE-master_source_code. Of course, you can fix the tmhmm_usearch.py scripts according your environment.

For the database "Ecoli_xray_nmr_pdb_no_nesg_simple_id.fasta" that necessary for Usearch, please refer to Jiri Hon et al., 2021(https://doi.org/10.1093/bioinformatics/btaa1102).

2.3.3 Running

Changing working dir to DeepSoluE-master_source_code, and then running the following command:

python DeepSoluE.py -i testing.fasta -o prediction_results.csv

-i: name of input_file in fasta format # folder “sequence” is the default file path of the input_file

-o name of output_file # folder “results” is the default file path for result save.

3. Output explaining

The output file (in ".csv" format) can be found in results folder, which including sequence number, sequence_id, predicted probability and pedicted result.

4. References

Chao Wang et al. 2022. DeepSoluE: A LSTM model for protein solubility prediction using sequence physicochemical patterns and distributed representation information (Submited).